Building responsible AI before it disintegrates us

By Evans Sagomba | 16h ago

ETHICAL artificial intelligence (AI) is not optional; it is the foundation of trustworthy intelligence.

And here is the hard truth: Zimbabwe does not have the luxury of learning from ruin first. We are already deploying algorithms, quietly, steadily, in places that touch people’s lives.

Mobile lenders crunch your data trail before you see an offer. Hospitals flirt with triage tools. Recruiters lean on automated filters. Ministries talk up “e‑government”, while private firms experiment with chatbots to cut support queues.

If we let the technology set the pace and tidy up the ethics later, we will discover the bill always arrives, usually at the expense of the least powerful.

What should guide us? Five pillars. Not slogans, not window-dressing, but practical commitments that give Zimbabwe a fair shot at reaping digital dividends without deepening old wounds.

Let us talk plainly about what they must mean here, in Zimbabwe, where power, scarcity and ingenuity twist together every day.

Fairness, bias mitigation

It sounds abstract until you picture a job-seeker in Kuwadzana filtered out because her CV lacks “elite” markers that an overseas model has learned to prize.

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Or a farmer in Gokwe denied a loan because his phone’s activity looks “risky” to an algorithm trained on South African urban data. Bias is seldom a moustache-twirling villain; it is mostly lazy assumptions baked into data and design.

If the AI training over-represents Harare, under-represents the rural and informal, and ignores dialects, the model will do the same. So, we must demand local data audits, representative sampling across provinces, languages, and income bands, and continuous monitoring of outcomes by subgroup, gender, age, disability and location.

When data disparities appear, they must trigger retraining and remediation, not public relations (PR) statements. And let us be honest: Fairness costs money.

It means paying enumerators, partnering with local universities and resisting the temptation to import a shiny model that has never met a kombi queue.

Transparency, explainability

Trust collapses where people cannot see how decisions are made. If a bank rejects your loan because an algorithm flagged something “off,” you deserve more than a shrug.

You deserve a clear reason and a way to challenge it. Explainability is not a luxury; it is due process in a digital age. That means building with interpretable components where possible, documenting assumptions, versioning models and creating audit trails that an internal reviewer and, when necessary, an external regulator can follow.

For our Zimbabwean context, let us translate that into practices: Plain-language notices at the point of decision; a simple appeals portal that works on a basic Android phone; and an independent oversight body with technical competence, not just a rubber stamp.

We cannot copy‑paste foreign templates and hope for the best. Our explainability must be home grown.

Privacy, data protection

We say “by design”, and then we keep every scrap of data on the off chance, it might be useful later. That mindset has to go. Data minimisation should be the default, not a reluctant concession.

Ask only for what is necessary, store it securely, encrypt traffic end-to-end and delete data when the purpose expires. Consent must be real, no buried clauses, no coercive “agree or lose service”.

Zimbabwe’s experience with leakages and misuse, from SIM registration databases to social media scraping, should have cured us of laxity. Privacy is not merely a personal preference; it is a collective shield. When one dataset leaks, entire Zimbabwean communities are profiled, targeted, and manipulated.

If we are serious about a functional digital economy, we must be serious about fines and sanctions for those who treat data as a free for all. And the Zimbabwean government must hold itself to the same, or higher, standard than it demands of business.

Accountability, governance

AI should never be a black box with no owner. Someone must be answerable for outcomes, good and bad. That starts with clear roles inside organisations: An accountable executive for each AI system, a risk register that is actually maintained and escalation paths that the public can use without insider connections.

Governance is not a three‑ring binder gathering dust; it is model cards that tell you what a system was trained on, a change log when it’s updated and a closing plan when the risks outweigh the benefits.

For public-sector deployments, parliament and civil society must be able to ask hard questions and expect timely answers. Procurement should require impact assessments before rollout, be published for public comment and include red‑team exercises to probe failure modes.

We learned, painfully, what happens when major systems go live without robust oversight. Let us not repeat it with technology that learns and shifts under our feet.

Safety, human oversight

Machines should augment human judgment, not replace responsibility. That is easiest to say in a conference room and hardest to live with on a busy ward or a cash‑strapped call centre.

The only way it holds is with discipline: Pre‑deployment testing that reflects real users and real constraints; guardrails that throttle decisions when data quality is poor; and human‑in‑the‑loop checkpoints for high‑stakes calls, loan denial, arrest alerts and clinical triage.

We should set up incident reporting channels such as aviation, where near‑misses are catalogued, shared and used to strengthen practice across the sector.

And when harm occurs, there must be a route to remedy that does not require the victim to hire a legal representative.

Now, a difficult point: Ethics is not a drag on innovation. It is a multiplier. A clinic will try a triage tool sooner if nurses know they can understand, override and report it.

A bank will scale a lending model faster if customers can appeal and win when the machine errs. A ministry will get more citizen uptake if the service respects privacy and explains choices.

In a market where trust is thin, reliability is the killer feature. The firms that figure that out will earn loyalty that no marketing budget can buy.

What would it look like to do this properly, Zimbabwe‑style? Start with a practical charter that any organisation can adopt and publish. It should commit to those five pillars and specify how they will be measured.

Set up a small, nimble, technically credible oversight unit, independent enough to call out both state and corporate abuse, close enough to reality to be useful.

Seed a network of university labs in Bulawayo, Mutare and Harare to run bias tests, security audits and red‑team exercises, funded by a mix of public money and industry levies.

Incentivise compliance through procurement, no ethical assurance, no contract. Encourage open dialogue: Public registers of AI systems in use and regular transparency reports that a lay reader can actually digest on a Sunday afternoon.

Inside organisations, we can adopt habits that travel well even where resources are tight.
Run small pilots instead of big‑bang deployments;
Measure outcomes by subgroup from day one;
Rotate cross‑functional review teams so that engineers, lawyers, clinicians and community reps look at the same dashboard and talk to each other;
Write playbooks that survive staff turnover;
Train front‑line workers to spot failure patterns and stop a rollout midstream if necessary; and
Tighten vendor contracts to require retraining on local data, data deletion on exit and third‑party audits that are binding.

For developers, a word. The code is not neutral.

Sagomba is a doctor of philosophy, who specialises in AI, ethics and policy researcher, AI governance and policy consultant, ethics of war and peace research consultant, political philosophy and also a chartered marketer. — esagomba@ gmail.com/ LinkedIn; @Dr. Evans Sagomba/ X: @esagomba.